Data & BI Lead

Overview:

We are seeking a Data & BI Lead with 10+ years of experience across data engineering, data warehousing, and business intelligence. This role requires strong hands-on expertise in modern cloud data platforms such as Microsoft Fabric, Azure Databricks, or Snowflake, along with deep experience in Power BI for building enterprise-grade analytical solutions.

The ideal candidate will lead and deliver end-to-end data platforms and BI solutions, with equal depth in data engineering, data modelling, and BI engineering (Power BI)

Role & responsibilities

• Lead design and implementation of scalable data platforms (lakehouse / warehouse) on Microsoft Fabric, Azure Databricks, or Snowflake
  
• Build and optimize data ingestion, transformation, and ELT pipelines for large-scale data processing
  
• Design and implement enterprise data models across:
• Data warehouse / lakehouse layers
  
• Analytical / semantic layers for reporting
  
• Develop and maintain Power BI datasets, semantic models, and dashboards aligned with enterprise standards
  
• Drive query and performance optimization across:
• SQL workloads and data pipelines
  
• BI semantic layer and reporting performance
  
• Establish data warehousing standards, modelling frameworks, and reusable data assets
  
• Ensure data quality, governance, lineage, and security across the platform
  
• Collaborate with stakeholders to translate business requirements into scalable data and analytics solutions
  
• Provide technical leadership across both data engineering and BI workstreams
  
• Drive DevOps and CI/CD practices for data pipelines and BI deployments

Preferred candidate profile

• 10+ years in Data Engineering, Data Warehousing, BI, or Analytics
  
• Strong hands-on expertise in data engineering on at least one platform (mandatory):
• Microsoft Fabric (Lakehouse, Dataflows, Pipelines)
  
• Azure Databricks (PySpark, Delta Lake, distributed processing)
  
• Snowflake (ELT design, performance optimization, data modelling)
  
• Very strong SQL expertise (mandatory):
• Complex transformations, query optimization, and performance tuning
  
• Experience handling large-scale, high-volume datasets
  
• Deep expertise in Data Modelling (mandatory):
• Dimensional modelling (star/snowflake schema)
  
• Normalization vs denormalization strategies
  
• Semantic layer design for analytics
  
• Strong Data Warehousing experience (mandatory):
• Designing and implementing enterprise-grade data warehouses
  
• Strong understanding of Kimball/Inmon methodologies
  
• Experience with lakehouse architectures
  
• Strong Power BI experience (mandatory):
• End to end development expertise
  
• Building semantic models, reports, and dashboards
  
• Advanced DAX and Power Query (M)
  
• Performance tuning (aggregations, incremental refresh, composite models)
  
• Experience with:
• Data orchestration tools (e.g., Azure Data Factory / Synapse or equivalent)
  
• Python / PySpark for data processing
  
• CI/CD and DevOps practices for data and analytics solutions
  
• Data governance, lineage, and security (including RLS/OLS)